Financial Forecasting, Analysis and Modelling. Michael Samonas. Читать онлайн. Newlib. NEWLIB.NET

Автор: Michael Samonas
Издательство: John Wiley & Sons Limited
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Жанр произведения: Зарубежная образовательная литература
Год издания: 0
isbn: 9781118921098
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formulae and calculations that the model needs to perform.

      As a final note to this specific modelling exercise, we mentioned previously that there was no need for any decision making. The model was constructed simply to enhance the business understanding of a particular company policy. Should any decision need to be taken about which credit policy is more efficient, we could model a number of different scenarios each with various credit policies. For example we could examine 3 different policies (2/10 net 30, 2/10 net 45, and 2/10 net 60) in order to choose the most favourable one.

      1.3 THE FINANCIAL MODELLING PROCESS OF MORE COMPLEX MODELS

The financial modelling process is comprised of 4 steps as shown in Exhibit 1.1:

Exhibit 1.1 The 4 fundamental steps of the financial modelling process

      Let us examine each of the above steps in detail.

      1.3.1 Step 1: Defining the Problem the Model Will Solve: The Fundamental Business Question

      Financial modelling is used, as we mentioned previously, in order to solve various problems. The first step of the process includes teams or individuals asking the right questions at the start of the problem-solving process. This is sometimes hard to believe as it often seems that people are trying to solve a problem before they have properly defined it. Asking the right questions helps break down the problem into simpler constituents.

      For example the commercial manager of the company requests the financial analyst to present the impact on the bottom line results of the company of a New Product Development (NPD). Let us say that the costs of the whole NPD process are available and can be largely funded through government subsidy. In order to tackle the problem the financial analyst needs to ask the following questions:

      1 What will be the forecast sales volume of the new product per year?

      2 What will be the unit price?

      3 What will be the credit terms?

      4 What will be the inventory needs of the product?

      5 What will be the payment terms of the suppliers of the raw materials?

      6 What will be the incremental variable and fixed cost per year for the proposed production?

      7 When is it anticipated that the governmental subsidy for the initial investment costs will be received?

The problem, then, can be broken down as per Exhibit 1.2:

Exhibit 1.2 Breaking down a business problem into simpler constituents

      1.3.2 Step 2: Specification of the Model

      Now we have identified the variables of the problem, we need a solid and thorough specification for a successful financial modelling process. The major assumptions should be documented and organized by category (such as market prices, sales volumes, costs, credit terms, payment terms, capital expenditures, and so on). All assumptions should be placed separately on a single sheet so that we do not have to hunt through formulae to figure out where a number came from.

      Moreover, the specification of the model, depending on the problem we have to address, might include the following:

      ○ To formulate the standard financial statements, including the income statement, balance sheet, and statement of cash flow. For the problem described in Step 1, the balance sheet and cash-flow statements are used to determine the level of additional borrowing, although they are more time consuming than a plain income statement, provided that the new product development will be funded by debt. The interest expense of this borrowing is an expense line in the income statement that we need to forecast in order to answer the original question. In other cases, i.e. where a valuation is required, we would have to derive both the free cash flow and the Weighted Average Cost of Capital schedules as well.

      ○ To decide the time frame of our forecast and its granularity (time periods). This refers to whether calculations will be done at the monthly level of detail or on a yearly basis. This is important when projecting cash flows in order to ensure enough liquidity to withstand cash-flow spikes due to factors such as inventory replenishment, slow accounts receivable cycles, large quarterly tax payments, major capital purchases, and other events. Output results are normally monthly for the first forecast year, quarterly for the next, and annual for the rest of a full 5-year plan.

      ○ To group operating expenses by departments as appropriate for the specific industry. Typical departments might be General and Administrative, Sales & Marketing, Research & Development, or Operations. This allows a comparison of departmental expenses as a percentage of total expenses with other companies in the industry.

      ○ To decide which Key Performance Indicators (KPI) need to be calculated in order to address the problem in question. KPIs expressed as ratios such as revenue EBITDA cover or the quick ratio allow projections to be benchmarked against other companies in the industry.

      ○ To create various scenarios, in order to assess the impact of different strategies. That is, to evaluate a series of different model output variables given a set of different input variables.

      ○ To create a sensitivity analysis that shows what will be the impact of changing the major assumptions by equal amounts, in percentage terms. This allows us to determine which assumptions have the greatest impact on our forecast, and must therefore be thought out most carefully. It will also allow us to focus on the important model variables rather than getting lost among all model variables.

      ○ Finally, to create a control panel, i.e. a one-page summary where we can change the most important assumptions and see immediately how this impacts on the KPIs of interest.

      The importance of this step is to ensure that the proposed model is easy to read, easy to understand, easy to change, and simply easy to use. The way to make a model useful and readable is to keep it simple. The complexity of the transaction which has to be modelled and the complexity of the model itself are 2 different things.

      1.3.3 Step 3: Designing and Building the Model

      Designing and building the financial model is the next phase of the process. The specification phase (Step 2) should lay out the structure of the model in detail. In this step we first identify the outputs of the model. It is good practice to present the output of the model on a separate sheet. This output sheet is a combination of model inputs and formulae and should read directly from the workings sheet of the model. There may be more than one output sheet in case the resolution of step 1 requires the handling of uncertainty and creating sensitivity analyses. Moreover, the control panel described in the specification part of the modelling process, if any, is part of the output of the model. Next we build the assumptions sheet that forms the inputs to our model. If our model needs past data to build on, i.e. historic financial statements, we collect them and adjust them to the right level of detail. Depending on the problem we have to solve we will not need all of the income statement, balance sheet, and cash flow statement accounts, and thus some will need to be grouped together. Finally we build the workings or calculation sheets and fill their cells with formulae. Thus, the sheets where the calculations are taking place should always be separate from both the input and the output sheets. Also no hard-coded values should be typed directly into the calculations of the workings sheet. In case the purpose of the model is to forecast future financial statements, all the relevant key drivers of the historic financial statements are calculated and forecast into the future. The forecast key drivers will make the building blocks of the future financial statements as we will see in Chapter 3 where we will build a financial model from scratch in order for the reader to grasp all the aforementioned abstract rules.

      The following indicate best practices that will help you build models that are robust, easy to use, easy to understand, and painless to update. Best practices are of greatest concern when documents are used by more than one person:

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